mlsanityData to 14 Oct 2026

Vertical Federated Learning with Missing Features During Training and Inference

arXiv 2410.22564 ↗First posted 29 Oct 2024cs.LG cs.DC cs.DScited by 6

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Abstract (arXiv)

Vertical federated learning trains models from feature-partitioned datasets across multiple clients, who collaborate without sharing their local data. Standard approaches assume that all feature partitions are available during both training and inference. Yet, in practice, this assumption rarely holds, as for many samples only a subset of the clients observe their partition. However, not utilizing incomplete samples during training harms generalization, and not supporting them during inference limits the utility of the model. Moreover, if any client leaves the federation after training, its partition becomes unavailable, rendering the learned model unusable. Missing feature blocks are therefore a key challenge limiting the applicability of vertical federated learning in real-world scenarios. To address this, we propose LASER-VFL, a vertical federated learning method for efficient training and inference of split neural network-based models that is capable of handling arbitrary sets of partitions. Our approach is simple yet effective, relying on the sharing of model parameters and on task-sampling to train a family of predictors. We show that LASER-VFL achieves a O(1/√T) convergence rate for nonconvex objectives and, under the Polyak-Lojasiewicz inequality, it achieves linear convergence to a neighborhood of the optimum. Numerical experiments show improved performance of LASER-VFL over the baselines. Remarkably, this is the case even in the absence of missing features. For example, for CIFAR-100, we see an improvement in accuracy of 19.3% when each of four feature blocks is observed with a probability of 0.5 and of 9.5% when all features are observed. The code for this work is available at https://github.com/Valdeira/LASER-VFL.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 19 of its 34 references that are in the map. First posted on arXiv 29 Oct 2024.

Builds on

19 of 34 references.

References · 19

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 7,005
  3. 2022Vertical Federated Learning: Concepts, Advances and Challengescited by 108
  4. 2022In Defense of the Unitary Scalarization for Deep Multi-Task Learningcited by 67
  5. 2020Multi-Participant Multi-Class Vertical Federated Learningcited by 47
  6. 2020SplitNN-driven Vertical Partitioningcited by 42
  7. 2022An Extensive Data Processing Pipeline for MIMIC-IVcited by 40
  8. 2020FedCVT: Semi-supervised Vertical Federated Learning with Cross-view Trainingcited by 32
  9. 2021Privacy-preserving Federated Adversarial Domain Adaption over Feature Groups for Interpretabilitycited by 28
  10. 2023Vertical Federated Knowledge Transfer via Representation Distillation for Healthcare Collaboration Networkscited by 19
  11. 2021Practical Lossless Federated Singular Vector Decomposition over Billion-Scale Datacited by 18
  12. 2022A Hybrid Self-Supervised Learning Framework for Vertical Federated Learningcited by 17
7 more
  1. 2023Communication-Efficient Vertical Federated Learning with Limited Overlapping Samplescited by 13
  2. 2022Vertical Semi-Federated Learning for Efficient Online Advertisingcited by 12
  3. 2023Robust and IP-Protecting Vertical Federated Learning against Unexpected Quitting of Partiescited by 7
  4. 2024A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspectivecited by 7
  5. 2022VFed-SSD: Towards Practical Vertical Federated Advertisingcited by 5
  6. 2024Communication-efficient Vertical Federated Learning via Compressed Error Feedbackcited by 3
  7. 2023Robust Collaborative Inference with Vertically Split Data Over Dynamic Device Environmentscited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 4; 2026: 2.

6 papers.

  1. 2025FLOSS: Federated Learning with Opt-Out and Straggler Supportcited by 1
  2. 2026Heterogeneity-Aware Knowledge Sharing for Graph Federated Learningcited by 1
  3. 2025Deep Latent Variable Model based Vertical Federated Learning with Flexible Alignment and Labeling Scenarioscited by 0
  4. 2025X-VFL: A New Vertical Federated Learning Framework with Cross Completion and Decision Subspace Alignmentcited by 0
  5. 2025Hierarchical Dual-Strategy Unlearning for Biomedical and Healthcare Intelligence Using Imperfect and Privacy-Sensitive Medical Datacited by 0
  6. 2026Mixture of Predefined Experts: Maximizing Data Usage on Vertical Federated Learningcited by 0